class_docstr: The MissingValues Bar visualizer creates a bar graph that lists the total
  count of missing values for each selected feature column.
  
  When y targets are supplied to fit, the output is a stacked bar chart where
  each color corresponds to the total NaNs for the feature in that column.
  
  Parameters
  ----------
  alpha : float, default: 0.5
      A value for bending elments with the background.
  
  marker : matplotlib marker, default: |
      The marker used for each element coordinate in the plot
  
  color : string, default: black
      The color for drawing the bar chart when the y targets are not passed to
      fit.
  
  colors : list, default: None
      The color palette for drawing a stack bar chart when the y targets
      are passed to fit.
  
  classes : list, default: None
      A list of class names for the legend.
      If classes is None and a y value is passed to fit then the classes
      are selected from the target vector.
  
  kwargs : dict
      Keyword arguments that are passed to the base class and may influence
      the visualization as defined in other Visualizers.
  
  Attributes
  ----------
  features_ : np.array
      The feature labels ranked according to their importance
  
  classes_ : np.array
      The class labels for each of the target values
  
  Examples
  --------
  
  >>> from yellowbrick.contrib.missing import MissingValuesBar
  >>> visualizer = MissingValuesBar()
  >>> visualizer.fit(X, y=y)
  >>> visualizer.show()
docstr: Draws a horizontal stacked bar chart with different colors
  for each count of nan values per label.
docstr_tok: ["Draws","a","horizontal","stacked","bar","chart","with","different","colors","for","each","count","of","nan","values","per","label","."]
code: def draw_stacked_bar(self, nan_col_counts):
      """Draws a horizontal stacked bar chart with different colors
      for each count of nan values per label.
      """
      for index, nan_values in enumerate(nan_col_counts):
          label, nan_col_counts = nan_values
  
          if index == 0:
              # first draw should be at zero
              bottom_chart = np.zeros(nan_col_counts.shape)
  
          # if features passed in then, label as such
          if self.classes_ is not None:
              label = self.classes_[index]
  
          color = self.colors[index]
  
          self.ax.barh(
              self.ind - self.width / 2,
              nan_col_counts,
              self.width,
              color=color,
              label=label,
              left=bottom_chart,
          )
  
          # keep track of counts to build on stacked
          bottom_chart = nan_col_counts
  
file_path: yellowbrick/yellowbrick/contrib/missing/bar.py
imports: import numpy
  from yellowbrick.style.palettes import color_palette
  from .base import MissingDataVisualizer
label_desc: Use image node_id 4 for calling the MissingValuesBar obj's underlying member method code with example usage: obj.draw_stacked_bar(nan_col_counts) without return types